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Jean-Roch Vlimant

California Institute of Technology

The Tracking Machine Learning challenge : Throughput phase


May 03, 2021
Sabrina Amrouche, Laurent Basara, Paolo Calafiura, Dmitry Emeliyanov, Victor Estrade, Steven Farrell, Cécile Germain, Vladimir Vava Gligorov, Tobias Golling, Sergey Gorbunov, Heather Gray, Isabelle Guyon, Mikhail Hushchyn, Vincenzo Innocente, Moritz Kiehn, Marcel Kunze, Edward Moyse, David Rousseau, Andreas Salzburger, Andrey Ustyuzhanin, Jean-Roch Vlimant

* submitted to Computing and Software for Big Science 

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Physics and Computing Performance of the Exa.TrkX TrackML Pipeline


Mar 11, 2021
Xiangyang Ju, Daniel Murnane, Paolo Calafiura, Nicholas Choma, Sean Conlon, Steve Farrell, Yaoyuan Xu, Maria Spiropulu, Jean-Roch Vlimant, Adam Aurisano, Jeremy Hewes, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Markus Atkinson, Mark Neubauer, Gage DeZoort, Savannah Thais, Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu, Alex Ballow, and Alina Lazar


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MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks


Jan 21, 2021
Joosep Pata, Javier Duarte, Jean-Roch Vlimant, Maurizio Pierini, Maria Spiropulu

* 12 pages, 10 figures 

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Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics


Dec 08, 2020
Raghav Kansal, Javier Duarte, Breno Orzari, Thiago Tomei, Maurizio Pierini, Mary Touranakou, Jean-Roch Vlimant, Dimitrios Gunopoulos

* 9 pages, 4 figures, 4 tables, To appear in Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020) 

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Distributed Training and Optimization Of Neural Networks


Dec 03, 2020
Jean-Roch Vlimant, Junqi Yin

* 20 pages, 4 figures, 2 tables, Submitted for review. To appear in "Artificial Intelligence for Particle Physics", World Scientific Publishing 

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Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning


Oct 05, 2020
Cheng Chen, Olmo Cerri, Thong Q. Nguyen, Jean-Roch Vlimant, Maurizio Pierini

* 15 pages, 12 figures 

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Track Seeding and Labelling with Embedded-space Graph Neural Networks


Jun 30, 2020
Nicholas Choma, Daniel Murnane, Xiangyang Ju, Paolo Calafiura, Sean Conlon, Steven Farrell, Prabhat, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Panagiotis Spentzouris, Jean-Roch Vlimant, Maria Spiropulu, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy Usher

* Proceedings submission in Connecting the Dots Workshop 2020, 10 pages 

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Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark


May 04, 2020
Oliver Knapp, Guenther Dissertori, Olmo Cerri, Thong Q. Nguyen, Jean-Roch Vlimant, Maurizio Pierini

* 16 pages, 9 figures 

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Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics


Jan 08, 2020
Dawit Belayneh, Federico Carminati, Amir Farbin, Benjamin Hooberman, Gulrukh Khattak, Miaoyuan Liu, Junze Liu, Dominick Olivito, Vitória Barin Pacela, Maurizio Pierini, Alexander Schwing, Maria Spiropulu, Sofia Vallecorsa, Jean-Roch Vlimant, Wei Wei, Matt Zhang

* 26 pages, 38 figures. Corrected typos and added additional references in v2. Extended Acknowledgements section in v3 

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Quantum adiabatic machine learning with zooming


Aug 13, 2019
Alexander Zlokapa, Alex Mott, Joshua Job, Jean-Roch Vlimant, Daniel Lidar, Maria Spiropulu

* 7 pages, 4 figures 

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Charged particle tracking with quantum annealing-inspired optimization


Aug 13, 2019
Alexander Zlokapa, Abhishek Anand, Jean-Roch Vlimant, Javier M. Duarte, Joshua Job, Daniel Lidar, Maria Spiropulu

* 18 pages, 21 figures 

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Variational Autoencoders for New Physics Mining at the Large Hadron Collider


Dec 05, 2018
Olmo Cerri, Thong Q. Nguyen, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

* 17 pages, 9 figures, 4 tables 

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Topology classification with deep learning to improve real-time event selection at the LHC


Aug 01, 2018
Thong Q. Nguyen, Daniel Weitekamp III, Dustin Anderson, Roberto Castello, Olmo Cerri, Maurizio Pierini, Maria Spiropulu, Jean-Roch Vlimant

* 15 pages, 9 figures, 2 tables 

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Machine Learning in High Energy Physics Community White Paper


Jul 08, 2018
Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata

* Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm 

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An MPI-Based Python Framework for Distributed Training with Keras


Dec 16, 2017
Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu

* 4 pages, 4 figures, 1 table, [email protected], SC17 

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